AI systems and agentic workflows
Move AI from impressive to dependable.
Design RAG systems, MCP integrations, and agentic workflows around real users, trusted data, observable behaviour, security boundaries, and measurable outcomes.
Scope
- Use cases and workflow design
- Retrieval and knowledge architecture
- Tool and MCP integration
- Agent orchestration
- Evaluation and guardrails
- Human-in-the-loop design
- Security and observability
- Production deployment
Deliverables
- workflow and use-case definition tied to a business outcome
- RAG or agent architecture with data and integration plan
- evaluation strategy with concrete test sets
- security, guardrail, and observability plan
- a thin working vertical slice in your environment
Typical engagement
AI Production Readiness Sprint
For companies with an AI concept or prototype that needs a credible path to production.
Suggested duration: 2–4 weeks
Selected experience
AI
Enterprise RAG and agent knowledge platform
Designed a knowledge platform that turns internal documents, code, and operational knowledge into governed AI workflows, combining RAG, MCP tools, LangGraph orchestration, and LangSmith observability.
- RAG
- MCP
- LangGraph
- LangSmith
Product engineering
IoT and energy data platform
Designed data ingestion and analysis workflows for distributed meters and sensors, combining device integration, time-series data, automation, dashboards, anomaly detection, and AI-assisted insights.
- MQTT
- Time-series DB
- Node.js
- Automation
AI
ESP32 edge AI acoustic sensing node
Designed and built a field-ready ESP32-S3 edge device that runs acoustic ML inference locally, reports compact LoRaWAN telemetry, captures labelled training data, and gives operators a local web console for deployment and recovery.
- ESP32-S3
- ESP-IDF
- TensorFlow Lite Micro
- LoRaWAN
FAQ
Do we need an agent or ordinary workflow automation?
Often you need ordinary automation. Agents earn their place when the workflow genuinely requires judgement across steps — otherwise a deterministic pipeline is cheaper, faster, and easier to operate. Part of the first engagement is answering exactly this question honestly.
Can you work with our existing model provider?
Yes. The architecture is designed so the model provider is a replaceable component. Whether you use a hosted API, a cloud provider's models, or self-hosted open weights, the retrieval, tooling, evaluation, and guardrail layers stay the same.
How do you evaluate a RAG system?
With a test set built from real questions and documents, scored on retrieval quality and answer faithfulness separately. Evaluation is set up early so every retrieval or prompt change is measured, not eyeballed.
Can the system be self-hosted?
Yes. Self-hosting models, vector stores, and orchestration is a supported path — usually driven by data residency or procurement constraints. The trade-offs in cost and capability are documented before you commit.
How do you prevent an agent from accessing the wrong tools or data?
Tool access is scoped per workflow, not per model: each agent step gets an explicit allowlist, credentials with minimal permissions, input/output validation, and human approval gates where actions are irreversible. Access boundaries are part of the architecture, not a prompt instruction.
Curious what AI could actually do for your business?
Bring your questions, including the ones that feel too basic. In 30 minutes we go through how you work today, pick the task with the most to gain, and sketch what testing it would involve. No pitch, no obligation.
Prefer email? hello@nxtinno.com